School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China.
BMC Med Inform Decis Mak. 2022 Sep 2;22(1):230. doi: 10.1186/s12911-022-01976-6.
The diagnosis of cardiac abnormalities based on heart sound signal is a research hotspot in recent years. The early diagnosis of cardiac abnormalities has a crucial significance for the treatment of heart diseases.
For the sake of achieving more practical clinical applications of automatic recognition of cardiac abnormalities, here we proposed a novel fuzzy matching feature extraction method. First of all, a group of Gaussian wavelets are selected and then optimized based on a template signal. Convolutional features of test signal and the template signal are then computed. Matching degree and matching energy features between template signal and test signal in time domain and frequency domain are then extracted. To test performance of proposed feature extraction method, machine learning algorithms such as K-nearest neighbor, support vector machine, random forest and multilayer perceptron with grid search parameter optimization are constructed to recognize heart disease using the extracted features based on phonocardiogram signals.
As a result, we found that the best classification accuracy of random forest reaches 96.5% under tenfold cross validation using the features extracted by the proposed method. Further, Mel-Frequency Cepstral Coefficients of phonocardiogram signals combing with features extracted by our algorithm are evaluated. Accuracy, sensitivity and specificity of integrated features reaches 99.0%, 99.4% and 99.7% respectively when using support vector machine, which achieves the best performance among all reported algorithms based on the same dataset. On several common features, we used independent sample t-tests. The results revealed that there are significant differences (p < 0.05) between 5 categories.
It can be concluded that our proposed fuzzy matching feature extraction method is a practical approach to extract powerful and interpretable features from one-dimensional signals for heart sound diagnostics and other pattern recognition task.
基于心音信号的心脏异常诊断是近年来的研究热点。心脏异常的早期诊断对心脏病的治疗具有至关重要的意义。
为了实现心脏异常自动识别更实际的临床应用，我们提出了一种新颖的模糊匹配特征提取方法。首先，选择一组高斯小波，并基于模板信号进行优化。然后计算测试信号和模板信号的卷积特征。接着提取模板信号和测试信号在时域和频域中的匹配程度和匹配能量特征。为了测试所提出的特征提取方法的性能，构建了 K-最近邻、支持向量机、随机森林和多层感知机等机器学习算法，并使用基于心音图信号提取的特征进行心脏病识别。
结果发现，使用所提出方法提取特征的随机森林在十折交叉验证下的最佳分类准确率达到 96.5%。进一步评估了心音图信号的梅尔频率倒谱系数与我们算法提取的特征的组合。使用支持向量机时，综合特征的准确率、灵敏度和特异性分别达到 99.0%、99.4%和 99.7%，在基于相同数据集的所有报告算法中表现最佳。在几个常见特征上，我们使用了独立样本 t 检验。结果表明，5 类之间存在显著差异（p<0.05）。
可以得出结论，我们提出的模糊匹配特征提取方法是一种从一维信号中提取强大且可解释特征的实用方法，可用于心音诊断和其他模式识别任务。
